In recent years, with the increasing scale of power system, the operation complexity of power grid has been greatly increased as a result of many factors including the addition of intermittent energy resources, such as wind energy and solar energy, the construction of large-scale multi-level inter-basin hydropower plants, the integration of various-type powers, such as nuclear power, pumped storage power, and gas-turbine power, and the structure of power grid with AC-DC hybrid transmission over long distance. In the conventional power-system planning, in order to evaluate the adaptability and economy of future electricity planning, operating position of every generator unit on the load curve is arranged through production simulation technology. However, with the increasing variety of power supply structure and complexity of power grid structure, the power system operation is involved with many various constraints, such as peak load regulation, generator on-off constraints and securities of branch and interface power flow. The conventional production simulation technology is often difficult to consider all these operational constraints during practical operations. Therefore, a new technology called power system operation simulation appears. That is, for a given power planning scheme, the unit commitment model is used to consider all the operational constraints in the long-time operations of power system. This can refine the evaluation indexes for the current power planning program from many aspects, such as adaptability, economy and environmental protection in the future operation.
Currently, power system operation simulation technology without consideration of system security constraints is relatively mature, and some software products have appeared in abroad. Riso laboratory in Denmark has developed an operation simulation software for the power system, named Wilmar (WEBER Christoph, MEIBOM Peter. BARTH Rudiger, et al. WILMAR: A Stochastic Programming Tool to Analyze the Large-Scale Integration of Wind Energy. In: KALLRATH Josef. PARDALOS Panos M. REBENNACK Steffen, et al., Optimization in the Energy Industry, Energy Systems: Springer Berlin Heidelberg, 2009. p 437-458). This software evaluates system operation cost by hourly simulating the operation and is applied in the wind power integration planning and pumped storage planning. However, this software can only consider simulation of power source and does not model the power grid. Therefore, it cannot consider operation security constraints, such as securities of branch and interface power flow of the power system. GE Company has developed a software named MAPS which is able to achieve the chronological operation simulation of multi-area connected power systems considering the power delivery security constraints between different areas (http://www.geenergyconsulting.com/practice-area/software-products/maps). Oxford University has developed a software named Switch which can achieve similar functions (FRIPP Matthias. Switch: A Planning Tool for Power Systems with Large Shares of Intermittent Renewable Energy. Environmental Science & Technology, 2014, 46(11): 6371-6378.). Although branch power flow limits in the multi-area power system are considered in MAPS and Switch, MAPS and Switch can only divide the power system into several areas or tens of areas. Establishing network-flow model for power delivery between different areas cannot describe the detailed power flow of the power-grid. The simulation results can only analyze the rationality and economy of power supply structure and configuration in each area, and cannot analyze the security, rationality and economy of the power grid planning.
Considering system security constraints in the power system operation simulation brings great challenge to calculation and modeling. It is because that when the scale of the power grid is larger, node and branch of the power system that needs to be considered in the security-constraint power system are increasing. For example, generally, the power system for a middle-scale provincial network includes 100˜200 power generator units, and 1000˜2000 nodes, and 2000˜5000 branches. The amount of decision variables for daily unit commitment model in the operation simulation without considering security constraints will be more than ten thousand, and the number of constraints will be more than twenty thousand. When security constraints are included, the number of constraints will further increase to more than one-hundred thousand. It will cost great calculation and storage resources for such large-scale optimization model. Therefore, the calculation and storage resources become the bottleneck for practical use of the security-constrained power system operation simulation in the large-scale power system.
Therefore, it is needed to propose technologies to rapidly generate and calculate the security-constrained unit commitment model, based on current power system operation simulation technology, in order to increase calculating efficiency of the security-constrained power system operation simulation, which makes the security-constrained power system operation simulation able to apply to large-scale real-world power system. The related art of the present disclosure include:
1) Security-constraint power system operation simulation means that, certain scheduling objectives are selected according to power grid planning and power installed capacity planning with system load prediction and boundary conditions of power system operation formed by primary energy; and power system planning or power system operation mode is evaluated according to operation simulation result after a period of operation of the simulation system under generator unit operation constraints, system branch and interface power flow. Core of the power system operation simulation is to solve the unit commitment model daily or week-by-week and is expressed as mixed integer programming model, as following:min f(X,I)s.t. CPT+DIT≤b F≤GGPT−GDLT≤FPT≤PT≤PT 
In the above expressions, P and I are decision variables of the above optimal model. P is vector of output of different types of generator unit at each time period, and its elements are continuous variables. I is vector of state variables of generator units, and it elements are 0-or-1 variables. T is superscript denoting transposition of vector or matrix. The objective f(P,I) is the minimization of the total system operating cost, which includes the fuel, on-off, and load shedding costs. The constraint CPT+DIT≤b is the system operating constraint and generator unit operating constraint. The system operating constraint includes load-generation balance constraint and back-up balance constraint. The generator unit operating constraint includes generator unit output constraint, rate of change of output constraint, generator unit on-off constraint, and electric quantity constraint, etc. C is coefficient matrix in front of the decision variable P corresponding to generator unit output in the above constraints. D is coefficient matrix in front of the decision variable I corresponding to generator unit state in the above constraints, and b is right constant term vector in each above constraint. The constraint F≤GGPT+GDLT≤F is security constraints of branch and transformer power flow. GG is generator shifting distribution factor. L is vector of node loads. F and F are the upper and lower limits of branch power flow, respectively. The constraint PT≤PT≤PT is the upper and lower limit constraints of P. T is transposition symbol. The detailed model of the power system operation simulation can be referred to the paper (Ning Zhang, Chongqing Kang, Daniel S. Kirschen, Qing Xia, Weimin Xi, Junhui Huang, Qian Zhang: Planning pumped storage capacity for wind power integration. IEEE Transactions on Sustainable Energy, 2013, 4(2): 393-401).
2) Generator shifting distribution factor matrix and load shifting distribution factor matrix:
The generator shifting distribution factor matrix means sensitivity of the generator output to the branch power flow. The load shifting distribution factor matrix means sensitivity of the node load to the branch power flow. If there are K branches, N nodes and M generator units in the power system, the generator shifting distribution factor GG and the load shifting distribution factor GD can be expressed as following:
            G      G        =          [                                                  g              11                                                          g              12                                            …                                              g                              1                ⁢                M                                                                                        g              21                                                          g              22                                            …                                              g                              2                ⁢                M                                                                          ⋮                                ⋮                                ⋱                                ⋮                                                              g                              K                ⁢                                                                  ⁢                1                                                                        g                              K                ⁢                                                                  ⁢                2                                                          …                                              g              KM                                          ]        ,            G      D        =                  [                                                            g                11                                                                    g                12                                                    …                                                      g                                  1                  ⁢                  N                                                                                                        g                21                                                                    g                22                                                    …                                                      g                                  2                  ⁢                  N                                                                                        ⋮                                      ⋮                                      ⋱                                      ⋮                                                                          g                                  K                  ⁢                                                                          ⁢                  1                                                                                    g                                  K                  ⁢                                                                          ⁢                  2                                                                    …                                                      g                KN                                                    ]            .      
There are K rows and M columns in matrix GG. glm is sensitivity of generator unit m to branch l. There are K rows and N columns in matrix GD. gln is sensitivity of node n to branch l. If output vector X of all generator units and load L of all nodes are known, then branch power flow can be obtained by the matrix GG and GD, as following:F=GGPT−GDLT.
In the above expression, F is vector of branch power flow.
3) Mixed integer programming optimal solving algorithm: this algorithm can give optimal solution of the model using computer to solve the mixed integer programming optimal problem.